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Michael WalterView profile

Professor

• Quantum Information
• Tensor Networks
• Holography
• +7 more

Prof. Michael Walter is a Full Professor (W3 Chair) in Quantum Information at Ruhr University Bochum, affiliated with the Faculty of Computer Science and the Cluster of Excellence CASA. Previously, he held a Tenure-Track Assistant Professorship in Mathematics and Theoretical Physics at the University of Amsterdam (2017-2021) and was a Postdoctoral Scholar at Stanford University (2014-2017). He earned his Dr. sc. (PhD) from ETH Zurich in 2014 under Matthias Christandl, with a thesis on quantum state marginals. His research focuses on quantum information theory, tensor networks, and their connections to holography, complexity theory, and invariant theory. Education: Dipl. Math. from University of Karlsruhe (KIT) and Georg-August-University Göttingen (2005-2010), advised by Thomas Schick. Postdoctoral work at Stanford expanded his expertise in quantum many-body systems and computational methods. Research Interests: Quantum information’s intersection with geometry and algebra, including quantum entanglement, tensor network models of holography, and computational complexity. He explores applications of optimization theory and representation theory to quantum problems, such as the quantum marginal problem and non-commutative optimization. Awards: ERC Starting Grant (2022), Early Career Award (KNAW, 2020), NWO Veni Laureate (2017), and ETH Medal (2014). His work bridges quantum computing, mathematics, and theoretical physics, with contributions to quantum gravity via tensor networks and algorithmic approaches to quantum states. Grants & Leadership: Leads the Chair of Quantum Information, collaborating internationally on projects like holographic duality and quantum resource theories. His research group develops theoretical frameworks for quantum technologies and foundational quantum mechanics. Labs/Teams: Active in CASA (Cluster of Excellence) at Ruhr University, focusing on advanced data science and computational methods. Collaborates with institutions like QuSoft (Amsterdam) and IQC (Waterloo).

Linhao MengView profile

Researcher

• Machine Learning
• Visual Analytics
• Federated Learning
• +3 more

Linhao Meng is a Researcher affiliated with the School of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His work focuses on visual analytics, machine learning, and human-computer interaction, with applications in federated learning, uncertainty visualization, and model comparison. Recent publications demonstrate expertise in dimensionality reduction, spatio-temporal analysis, and social media data visualization. Research emphasizes interactive tools for model diagnosis and historical event reasoning. Key collaborations and citations highlight impact in computer science domains (Scopus h-index: 2, 163+ citations). Current projects include federated learning security analysis and visualization of historical spatio-temporal events.

• Applied Data Science
• Educational Development
• Practice-Oriented Educational Research
• +2 more

Claudy Oomen is an Educational Consultant/Trainer at the Educational Development & Training unit within the Faculty of Social and Behavioral Sciences at Utrecht University. Her work focuses on Applied Data Science in educational contexts, including practice-oriented research and collaborative projects between higher education institutions and schools. 2020: Evaluated bachelor selection procedures for PPE and UCU programs. 2017: Studied successful collaborations between educational institutions and published on learning organizations for School aan Zet. 2010-2015: Advised on quality frameworks for cultural education and language performance monitoring in secondary schools. 2001: Authored a doctoral thesis on self-monitoring in normal and aphasic speech (Utrecht University). Her research bridges data science applications with educational development, emphasizing practical outcomes and organizational learning. Publications span academic collaboration, speech pathology, and curriculum design. Contact: cceoomen@uu.nl or c.c.e.oomen@uu.nl .

Evanthia DimaraView profile

Assistant Professor

• Information Visualization
• Human-Computer Interaction
• Judgment and Decision-Making
• +3 more

Dr. Evanthia Dimara is a Tenured Assistant Professor in the Department of Information & Computing Sciences at Utrecht University's Faculty of Science. Her research focuses on Information Visualization and Human-Computer Interaction, particularly how technology can aid unbiased decision-making in individual and group contexts. Education : Ph.D. in Computer Science (2017) from INRIA & University Paris-Saclay M.Sc. in Human-Computer Interaction (2013) from University of Paris-Saclay B.Sc. in Informatics & Telecommunications (2012) from National and Kapodistrian University of Athens Her work addresses decision-making failures in current visualization systems, with a focus on cognitive biases. Recent publications analyze financial fraud detection frameworks, job-seeking tools, and public policy reasoning errors, emphasizing human-AI collaboration and visual analytics. Professional Activities : Associate Editor, IEEE Transactions on Visualization and Computer Graphics (2023–present) Chair, IEEE VIS Workshops (2023, 2017) Program Committee Member, IEEE VIS, BELIV, VisPsych

Michael BehrischView profile

Associate Professor

• Visual Analytics
• Big Data Analytics
• Information Visualization
• +3 more

Dr. Michael Behrisch is an Associate Professor for Visual Analytics in the Visualization and Graphics Group at Utrecht University's Department of Information and Computing Sciences. With a PhD from University of Konstanz, his career includes postdoctoral work at Harvard and Tufts Universities, and over six years as a research associate at Konstanz. Specializes in matrix-based representations for relational data Focuses on cognitive load reduction in visual analytics Develops interactive systems for pattern discovery Research Highlights: Combines algorithmic approaches with user-centric visualization techniques to address challenges in large-scale, multivariate, and dynamic datasets. Research themes include: Automated pattern quantification Matrix reordering algorithms Explainable AI integration Game research applications Scientific Contributions: Recognized through 61 publications and multiple awards, including the EuroVA 2022 Best Paper and IEEE VAST 2018 Honorable Mention. His work bridges theoretical research with practical applications across life sciences, network analysis, and big data domains.

U. GüçlüView profile

Researcher

• NeuroAI
• Neural Coding
• Deep Learning
• +3 more

Dr. U. Güçlü is a Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour at Radboud University, Netherlands. He leads the Neural Coding Lab and Donders AI for Neurotech Lab , focusing on bridging neuroscience and AI to simulate in vivo neural computation using in silico connectionism for "brain reading" and "brain writing." He ranks among the top 10 most-cited researchers in Neural Decoding, Neural Encoding, and NeuroAI on Google Scholar (H-index: 29). University: Radboud University Institution: Donders Institute Rank: Researcher His research interests include NeuroAI , Neural Coding , and Neurotechnology , particularly for restoring sensory and cognitive functions (e.g., vision in the blind, audition in the deaf). His articles highlight advancements in neural decoding, encoding models, and AI-driven prosthetic vision. He is an ELLIS Scholar and Computable Laureate . Scientific Awards : Computable Laureate Grants : 2023: ≈€250k internal research grant (co-applicant) 2022: ≈€20M NWO Gravitation grant (consortium member) 2020: ≈€500k internal grant (€250k co-applicant) 2019: ≈€250k internal grant 2016: ≈€1.5M NWO Perspective grant (consortium member)

Frans M.J. WillemsView profile

Professor

• Information Theory
• Data Compression
• Digital Communication
• +5 more

Frans M.J. Willems is a Full Professor Emeritus at Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems group within the Department of Electrical Engineering. His primary research focuses on information theory, including multi-user communication systems, data compression, digital communication, biometrics, and privacy/security aspects of information systems. Education: MSc in Electrical Engineering (1979), TU/e PhD in Mathematics (1982), University of Leuven Research Contributions: Willems has authored over 295 publications and holds multiple patents. His work spans foundational topics like context-tree weighting algorithms, biometric privacy frameworks, and MIMO communication systems. He served as IEEE Transactions editor (1988–2006) and became an IEEE Fellow in 2005. Projects: DIGI-OPT : Optical fiber communication technologies (2018–2026) MyWave : Energy-efficient massive MIMO systems (2019–2023) RESCURE : Cybersecurity for critical infrastructure (2018–2020) Awards: IEEE Fellow (2005). Advisory Roles: Advised Philips Research on information theory applications (1999–2018). Supervised 35 students and contributed to 11 major research projects. Labs/Teams: Leads the Signal Processing Systems group at TU/e, collaborating on topics like nonlinear optical channels and secure communication systems.

Matthias NauView profile

Assistant Professor

• Computational Cognitive Neuroscience
• Active Vision
• Memory
• +4 more

Matthias Nau is an Assistant Professor at the Department of Experimental and Applied Psychology within the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam. His work focuses on computational cognitive neuroscience, exploring how neural and behavioral signals shape human experiences through active vision, memory, and imagery. Fields of interest include computational cognitive neuroscience, active vision, memory, mental imagery, neuroimaging, behavioral tracking, and artificial intelligence. He develops open-access AI-powered tools to study human brain activity and behavior. Recent research trends highlight collaborations between cognitive neuroscience and AI, utilizing fMRI and eye tracking to decode task demands, sensorimotor timing, and spatial perception mechanisms. His 2024 paper in Nature Neuroscience emphasizes generalization across tasks. Scientific awards and grants: Feodor Lynen Research Fellowship (2021) NIH Visiting Fellowship (2021) He coordinates and teaches Brain Imaging and Programming for Psychologists in the Research Master Cognitive Neuropsychology program. Grants include AAPG Research Grant (2023), Fyssen Research Grant (2023), and iBBA Research Support Fund (2024). No ancillary activities are listed.

Heike TrautmannView profile

Professor

• Data Science
• Evolutionary Optimization
• Social Media Analytics
• +4 more

Heike Trautmann is a Full Professor of Data Science: Statistics and Optimization at the University of Münster, holding dual appointments in Datamanagement & Biometrics and the Digital Society Institute. She directs the European Research Center of Information Systems (ERCIS). Her academic journey includes a PhD (2004) and Habilitation (2013) in Statistics from Universitat Dortmund, focusing on optimization methodologies. Her research spans evolutionary optimization, machine learning, and algorithm selection, with applications in multi-objective problems and social media analytics. Recent work emphasizes deep learning-driven exploratory landscape analysis and hybrid algorithms for complex optimization challenges. Education: Habilitation in Statistics (2013): Universitat Dortmund PhD in Statistics (2004): Universitat Dortmund Master in Statistics (2000): Universitat Dortmund Her research interests prioritize advancing optimization techniques through interdisciplinary approaches, particularly leveraging machine learning for algorithm configuration and performance analysis. Collaborations highlight global networks addressing computational challenges in continuous and mixed-variable spaces. As ERCIS director, she drives innovation in information systems and societal impact through data science.

• Social Cognition
• Prejudice Formation
• Neural Mechanisms of Bias
• +2 more

Prof. David Amodio is a faculty member at the University of Amsterdam, affiliated with the Faculty of Social and Behavioural Sciences and the Programme group Social Psychology. His research investigates the psychological and neural mechanisms of social cognition, prejudice, and bias. His research focuses on how social attitudes form through learning mechanisms, the role of AI in propagating societal biases, and neural correlates of prejudice regulation. Key areas include instrumental learning of stereotypes, computational modeling of social cognition, and interventions to reduce bias. Analysis of his publications reveals consistent themes in social neuroscience, including neural encoding of prejudice, cross-cultural bias transmission, and computational approaches to social behavior. His work integrates experimental psychology with neuroimaging techniques to study real-world social issues. Scientific Awards: 2024 SESP Trajectory Award for mid-career contributions to social psychology As head of the Amodio Lab, he leads collaborative projects on social learning mechanisms and maintains partnerships with New York University. The lab prioritizes diversity and equity in scientific communities.

Federico de MartinoView profile

Professor

• Neuroimaging
• Auditory Perception
• Machine Learning
• +3 more

Federico de Martino is a Full Professor at Maastricht University's Faculty of Psychology and Neuroscience (FPN), specializing in Sensory Circuits and Neuroimaging. He holds an honorary researcher position at the Center for Magnetic Resonance Research (CMRR) in Minneapolis, USA. His research integrates computational models with high-resolution neuroimaging techniques to study auditory perception and predictive coding in the brain. Education: Graduated in Electronic Engineering (cum laude) from Sapienza University of Rome (2003), earned a PhD in Cognitive Neuroscience from Maastricht University (2008). Career milestones include roles as Post-Doctoral Fellow (2008-2011), Research Associate at CMRR (2009-2012), Assistant Professor (2011-2017), and Associate Professor (2017-2022) at FPN. Research interests focus on ultra-high field MRI (7T+), mesoscopic neural computations, and the functional organization of auditory pathways. Key projects include mapping auditory circuits at sub-millimeter resolution and investigating vascular underpinnings of fMRI signals. Collaborations span institutions like CMRR, University of Glasgow, and Max Planck Institute. Teaching roles include coordinating fMRI data analysis courses at FPN and the Maastricht Science Program. He co-chairs the Maastricht Brain Imaging Center (MBIC) Graduate School and leads FPN's MRI user support group. Active in science outreach as a founding member of the Maastricht Young Academy (MYA) and co-chair of the Growing Up in Science (GUS) initiative. Grants include an NWO VIDI grant (auditory pathway mapping), NIH Brain Initiative funding (laminar fMRI), and an ERC Consolidator grant (predictive listening). Recent publications emphasize high-resolution auditory imaging, predictive coding, and methodological advancements in neuroimaging.

• Machine Learning
• Remote Sensing
• Geoinformation Science
• +3 more

Marc Rußwurm is an Assistant Professor of Machine Learning and Remote Sensing at Wageningen University, affiliated with the Laboratory of Geo-information Science and Remote Sensing. He holds a Ph.D. in Remote Sensing Technology from Technical University of Munich (2022) and completed postdoctoral research at EPFL (2021-2023). His work focuses on applying modern machine learning methods to real-world remote sensing challenges, including vegetation classification using satellite time series and marine debris detection. He is particularly interested in domain shifts and transfer learning in geographic data. Education: Ph.D. (Dr.Ing), Technical University of Munich (2018-2021) Postdoctoral Researcher, Swiss Federal Institute of Technology Lausanne (EPFL, 2021-2023) Research Interests: Machine learning for remote sensing Satellite time series analysis Transfer learning and domain adaptation Marine debris detection Geospatial data modeling Recent Research Trends: His articles emphasize innovative applications of machine learning in environmental monitoring, including spectral index searches for marine debris, location embeddings via satellite imagery, and data-centric approaches for Earth observation. He also explores challenges in model extrapolation and cross-resolution analysis. Advising & Grants: Co-promotor for PhD candidates V. Nedungadi (Machine Learning for Agriculture) and C. Robin (Spatiotemporal Ecosystem Dynamics) Involved in collaborative projects on tropical forest loss and Antarctic ice mapping Labs/Teams: Active in the Laboratory of Geo-information Science and Remote Sensing at Wageningen University, contributing to interdisciplinary research on geospatial technologies.

Christin ScholzView profile

Researcher

• Persuasive Communication
• Social Neuroscience
• Polarization Dynamics
• +4 more

Dr. Christin Scholz is a Researcher at the University of Amsterdam’s Faculty of Social and Behavioural Sciences, affiliated with the Persuasive Communication department. She co-leads the ACHC Communication, Brain, and Society (CoBraS) Lab and contributes to the Research Priority Area on Polarisation. Her work bridges social psychology, neuroscience, and communication studies, focusing on cognitive biases, polarization dynamics, and the neural mechanisms underlying information sharing. Education: Holds a PhD in Persuasive Communication. Her research integrates neuroimaging (fMRI), ecological momentary assessment, and geolocation tracking to study health behaviors and societal polarization. Research Interests: Explores how cognitive biases like confirmation bias and dissonance drive polarization. Her studies investigate peer influence on drinking behavior, the role of mental well-being in health messaging, and the application of behavioral science to climate action. She employs interdisciplinary methods, including neuroimaging and global data collaboration across 63 countries. Key Contributions: Pioneered the GeoSmoking Study and the International Climate Psychology Collaboration. Her work highlights the societal impact of fragmented media ecosystems and advocates for epistemic vigilance in information processing. Labs & Teams: ACHC CoBraS Lab (focusing on communication and neuroscience) and the Amsterdam Center for Health Communication. Collaborates with political scientists, neuroscientists, and behavioral economists to address complex societal challenges.

Alfred Christiaan SchoutenView profile

Associate Professor

• Neuromuscular Control
• System Identification
• Motor Control
• +7 more

Alfred Christiaan Schouten is an Associate Professor at the TechMed Centre, Delft University of Technology, specializing in Biomechatronics and Rehabilitation Technology. His work bridges engineering and clinical neuroscience to advance rehabilitation technologies and understanding of human motor control. Institution: Delft University of Technology School: TechMed Centre Department: Biomechatronics and Rehabilitation Technology Academic Rank: Associate Professor His research focuses on neuromuscular control, system identification, and rehabilitation engineering, particularly in stroke recovery, joint impedance, and sensory feedback mechanisms. He applies advanced modeling and signal processing techniques to understand motor impairments and develop novel therapeutic interventions. The recent publications reflect a strong trend in neurorehabilitation, with emphasis on stroke, spasticity, balance control, and brain-computer interfaces. His work integrates experimental data with computational models, often involving EMG, EEG, and biomechanical measurements. Key themes include disentangling intrinsic and reflexive contributions to muscle stiffness, sensory reweighting, and developing open datasets for reproducible research. Scientific recognition includes: Best Student Paper Finalist (August 2018) He has supervised multiple research projects and datasets, indicating active mentoring and collaboration. His research is supported by data-driven methodologies and interdisciplinary teamwork, particularly within the fields of neuroengineering and rehabilitation robotics. He has presented his work internationally, contributing to conferences in acoustics, speech processing, and motor control. He leads or contributes to several research datasets, promoting open science and reproducibility in biomechanics and neuroengineering. His lab activities involve system identification, biofeedback development, and neurophysiological validation of joint dynamics.

Malvina NissimView profile

Professor

• Computational Linguistics
• Natural Language Processing (NLP)
• AI Ethics
• +3 more

Malvina Nissim is a Professor of Computational Linguistics and Society at the University of Groningen's Faculty of Arts, affiliated with the Center for Language and Cognition Groningen (CLCG). Her research focuses on NLP applications to societal challenges, ethics in AI, and science communication. She is actively involved in initiatives like the Sectorplan's Humane AI theme and the ACL Ethics Committee. Education: Not explicitly listed in provided texts, but her career path suggests doctoral training in linguistics/comp. ling. Research interests include multilingual NLP, bias mitigation in AI, ancient language processing (e.g., AGALMA project), and responsible tech communication. She has co-authored a popular introductory book on Computational Linguistics (in Italian). Publications span topics like dialogue systems, model uncertainty in ed tech, and cross-lingual NLP. Key awards include the 2016 UG Lecturer of the Year. Awards: 2016 Lecturer of the Year, multiple grants (e.g., CLICK-NL 800K for PAGINA project with Dagblad van het Noorden).